SRCGS模型与算法描述中国图象图形学报.DOC

中图法分类号：TP301.6 文献标识码：A 文章编号：1006-8961(201 ) - - 论文引用格式： 基于稀疏表示和贪婪搜索的人脸分类算法 刘梓1 ，宋晓宁1,2 ， 唐振民1 1.南京理工大学计算机科学与工程学院, 南京 210094；2.江南大学物联网学院, 无锡 214122 ） 摘 要：目的：随着稀疏表示方法在图像重建问题中的巨大成功，研究人员提出了一种特殊的分类方法，即基于稀疏表示的分类方法。约束，本文提出了稀疏分类框架下的迭代剔除机制和贪婪搜索策略的人脸识别方法。方法将测试样本表示成训练样本线性组合的方式，并在所有训练样本中通过迭代计算来消除对分类影响较小的类别和单个样本，在系数分解的过程中采用最小误差正交匹配追踪(Error-Constrained Orthogonal Matching Pursuit, OMP)算法，进而选择出贡献程度大的类别样本并进行分类。约束。在所有的实验中，正则化参数的取值为0.001，在ORL、FERET和AR三个人脸数据库上，本文的识别率可分别达到97.88%、67.95%和94.50%，进而验证了提出算法的有效性。稀疏分类框架下的迭代剔除机制和贪婪搜索策略的人脸识别方法lassification algorithm using greedy search strategy Liu Zi1, Song Xiaoning1,2, Tang Zhenmin1 1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094; 2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122) Abstract: Objective: The great success of sparse representation in image reconstruction triggers the research on sparse representation based pattern classification (SRC). Actually, a strict and standard dictionary will create sparser coefficient, but it might not achieve better classification accuracy in SRC model. Recently researches revealed that it is the collaborative representation (CR) mechanism, but not the -norm sparsity constraint, that truly improves the face recognition (FR) accuracy. Therefore, constructing a rational and optimal dictionary for SRC model is a challenging task for us. In the present study, we propose a novel SRC fusion method using dynamical class-elimination mechanism which strengthens the ability of collaborative representation and a greedy search strategy for face recognition. Method: The proposed method involves two aspects: training variables selection and classification strategy and sparse coding coefficient decomposing. The training variables selection and classification strategy aim to represent a query sample as a linear combination of the most informative training samples, and exploits an optimal representation of training s